Zhang Jingyao, Meng Deyuan
IEEE Trans Neural Netw Learn Syst. 2023 Dec;34(12):10398-10407. doi: 10.1109/TNNLS.2022.3166797. Epub 2023 Nov 30.
For systems executing repetitive tasks, how to realize the perfect tracking objective is generally desirable, for which an effective method called "iterative learning control (ILC)" emerges thanks to the incorporation of the repetitive execution of systems into an ILC design framework. However, nonrepetitive (iteration-varying) uncertainties are often inevitable in practice and greatly degrade the tracking accuracy of ILC, which has not been treated well, regardless of considerable robust ILC results. This motivates this article to develop a new design method to improve the tracking accuracy of ILC by adopting a high-order extended state observer (ESO) to address ill effects of nonrepetitive uncertainties and uncertain system models. With the designed ESO-based ILC, the robust tracking of any desired trajectory can be achieved such that the tracking error can be decreased to vary in a small bound depending continuously on the bounds of high-order variations of nonrepetitive uncertainties with respect to the iteration. It makes the tracking accuracy of ILC possible to be regulated through the design of ESO, of which the validity is demonstrated by including a simulation example.
对于执行重复任务的系统，通常希望实现完美的跟踪目标，为此，一种名为“迭代学习控制（ILC）”的有效方法应运而生，这是通过将系统的重复执行纳入ILC设计框架实现的。然而，非重复（迭代变化）不确定性在实际中往往不可避免，并且会大大降低ILC的跟踪精度，尽管已有相当多的鲁棒ILC成果，但这一问题仍未得到很好的解决。这促使本文开发一种新的设计方法，通过采用高阶扩张状态观测器（ESO）来解决非重复不确定性和不确定系统模型的不良影响，从而提高ILC的跟踪精度。利用所设计的基于ESO的ILC，可以实现对任何期望轨迹的鲁棒跟踪，使得跟踪误差能够减小到在一个小范围内变化，该范围连续依赖于非重复不确定性相对于迭代的高阶变化的界。这使得通过ESO的设计来调节ILC的跟踪精度成为可能，文中包含的一个仿真示例证明了其有效性。
